Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Inferring a Personalized Next Point-of-Interest Recommendation Model with Latent Behavior Patterns
Authors: Jing He, Xin Li, Lejian Liao, Dandan Song, William Cheung
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on two large-scale LBSNs datasets demonstrate the significant improvements of our model over several state-of-the-art methods. |
| Researcher Affiliation | Academia | 1BJ ER Center of HVLIP&CC, School of Comp. Sci., Beijing Institute of Technology, Beijing, China 2Department of Computer Science, Hong Kong Baptist University , Hong Kong, China EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Our Proposed Methodology |
| Open Source Code | No | The paper does not provide any statement regarding the release of source code or a link to a code repository. |
| Open Datasets | Yes | We choose two large-scale datasets from real-world LBSNs, Foursquare and Gowalla, to conduct the experiments. Foursquare check-in data is within Los Angeles, provided by (Bao, Zheng, and Mokbel 2012), while Gowalla dataset is from (Cheng et al. 2012) with a complete snapshot. |
| Dataset Splits | No | The paper states: 'For other parameters, we tune them in the training sets to ο¬nd the optimal values, and subsequently use them in the test set.' While this implies a form of validation for parameter tuning, it does not explicitly define a 'validation' dataset split with specific percentages or counts, or refer to a standard validation split. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU, memory, cloud services) used to run the experiments. |
| Software Dependencies | No | The paper describes the algorithms and models used (e.g., BPR, EM) but does not provide details on specific software dependencies, programming languages, or their version numbers used for implementation. |
| Experiment Setup | Yes | We set λΠto be 1 for both FPMCLR and our proposed model. The empirical settings of the number of latent behavior patterns are 4 and 6 for Gowalla dataset and Foursquare dataset, respectively. For other parameters, we tune them in the training sets to ο¬nd the optimal values, and subsequently use them in the test set. |